Ideas2IT Blog

Between October 2025 and March 2026, Anthropic, OpenAI, and Amazon Web Services each launched a purpose-built healthcare AI platform. Not general-purpose AI with a compliance disclaimer. Purpose-built, HIPAA-aligned products targeting clinical operations, revenue cycle, life sciences, and patient access.

Ask any director of institutional research or head of analytics at a mid-size university how their backlog looks. The answer is almost always some version of the same thing: too many requests, not enough people, and a queue that has been growing longer for at least two years. 

You already know the salary number. You've seen the Glassdoor ranges. You know that a strong senior data engineer in the US runs $135K–$170K base depending on market, stack, and experience. If you're in a high-cost metro or competing with FAANG for talent, you've seen higher. 

It's Monday morning. Your lead data engineer opens Slack and there are already 11 messages waiting. A Prefect flow failed overnight. A dbt model broke because someone upstream changed a schema without telling anyone. There's a ticket from Finance asking why last week's revenue numbers don't match what the dashboard showed on Friday. And somewhere in the backlog, buried under 23 maintenance items is the ML initiative you announced to the board last quarter. 

Your competitor shipped an AI assistant last quarter. Your board has asked three times in the last two meetings when you're shipping yours. Your VP of Product has a roadmap that includes "AI integration" in the next sprint cycle.

Somewhere in the last twelve months, the question changed. It used to be "should we be looking at AI?" Now it's "why aren't we further along?"